As SUPIR is not working yet, I will try to run another AI to be sure I can run an AI on ZERO space

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import cv2
+from moviepy.editor import VideoFileClip
+from share_btn import community_icon_html, loading_icon_html, share_js
+import torch
+from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
+from diffusers.utils import export_to_video
+
+
+
+def convert_mp4_to_frames(video_path, duration=3):
+    # Read the video file
+    video = cv2.VideoCapture(video_path)
+
+    # Get the frames per second (fps) of the video
+    fps = video.get(cv2.CAP_PROP_FPS)
+
+    # Calculate the number of frames to extract
+    num_frames = int(fps * duration)
+
+    frames = []
+    frame_count = 0
+    
+    # Iterate through each frame
+    while True:
+        # Read a frame
+        ret, frame = video.read()
+        
+        # If the frame was not successfully read or we have reached the desired duration, break the loop
+        if not ret or frame_count == num_frames:
+            break
+        
+        # Convert BGR to RGB
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Append the frame to the list of frames
+        frames.append(frame)
+
+        frame_count += 1
+
+    # Release the video object
+    video.release()
+
+    # Convert the list of frames to a numpy array
+    frames = np.array(frames)
+
+    return frames
+
+def infer(prompt, video_in, denoise_strength):
+
+    negative_prompt = "text, watermark, copyright, blurry, nsfw"
+
+    video = convert_mp4_to_frames(video_in, duration=3)
+    video_resized = [Image.fromarray(frame).resize((1024, 576)) for frame in video]
+
+    pipe_xl = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_XL", torch_dtype=torch.float32, revision="refs/pr/17")
+    pipe_xl.vae.enable_slicing()
+    pipe_xl.scheduler = DPMSolverMultistepScheduler.from_config(pipe_xl.scheduler.config)
+    pipe_xl.enable_model_cpu_offload()
+    pipe_xl.to("cpu")
+    video_frames = pipe_xl(prompt, negative_prompt=negative_prompt, video=video_resized, strength=denoise_strength).frames
+    del pipe_xl
+    #torch.cuda.empty_cache()
+    video_path = export_to_video(video_frames, output_video_path="xl_result.mp4")
+    
+    return "xl_result.mp4", gr.Group.update(visible=True)
+
+css = """
+#col-container {max-width: 510px; margin-left: auto; margin-right: auto;}
+a {text-decoration-line: underline; font-weight: 600;}
+.animate-spin {
+  animation: spin 1s linear infinite;
+}
+@keyframes spin {
+  from {
+      transform: rotate(0deg);
+  }
+  to {
+      transform: rotate(360deg);
+  }
+}
+#share-btn-container {
+  display: flex; 
+  padding-left: 0.5rem !important; 
+  padding-right: 0.5rem !important; 
+  background-color: #000000; 
+  justify-content: center; 
+  align-items: center; 
+  border-radius: 9999px !important; 
+  max-width: 13rem;
+}
+#share-btn-container:hover {
+  background-color: #060606;
+}
+#share-btn {
+  all: initial; 
+  color: #ffffff;
+  font-weight: 600; 
+  cursor:pointer; 
+  font-family: 'IBM Plex Sans', sans-serif; 
+  margin-left: 0.5rem !important; 
+  padding-top: 0.5rem !important; 
+  padding-bottom: 0.5rem !important;
+  right:0;
+}
+#share-btn * {
+  all: unset;
+}
+#share-btn-container div:nth-child(-n+2){
+  width: auto !important;
+  min-height: 0px !important;
+}
+#share-btn-container .wrap {
+  display: none !important;
+}
+#share-btn-container.hidden {
+  display: none!important;
+}
+img[src*='#center'] { 
+    display: block;
+    margin: auto;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            <h1 style="text-align: center;">Zeroscope XL</h1>
+            <p style="text-align: center;">
+            This space is specifically designed for upscaling content made from <br />
+            <a href="https://huggingface.co/spaces/fffiloni/zeroscope">the zeroscope_v2_576w space</a> using vid2vid. <br />
+            Remember to use the same prompt that was used to generate the original clip.<br />
+            For demo purpose, video length is limited to 3 seconds.
+            </p>
+            
+            [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg#center)](https://huggingface.co/spaces/fffiloni/zeroscope-XL?duplicate=true)
+            
+            """
+        )
+
+        video_in = gr.Video(type="numpy", source="upload")
+        prompt_in = gr.Textbox(label="Prompt", placeholder="This must be the same prompt you used for the original clip :)", elem_id="prompt-in")
+        denoise_strength = gr.Slider(label="Denoise strength", minimum=0.6, maximum=0.9, step=0.01, value=0.66)
+        #inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
+        submit_btn = gr.Button("Submit")
+        video_result = gr.Video(label="Video Output", elem_id="video-output")
+
+        with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
+            community_icon = gr.HTML(community_icon_html)
+            loading_icon = gr.HTML(loading_icon_html)
+            share_button = gr.Button("Share to community", elem_id="share-btn")
+
+    submit_btn.click(fn=infer,
+                    inputs=[prompt_in, video_in, denoise_strength],
+                    outputs=[video_result, share_group])
+    
+    share_button.click(None, [], [], _js=share_js)
+
+demo.queue(max_size=12).launch()